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Economy-Wide Effects of Reducing Illegal Immigrants in U.S. Employment

By: Dixon, Peter B.; Johnson, Martin et al. | Contemporary Economic Policy, January 2011 | Article details

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Economy-Wide Effects of Reducing Illegal Immigrants in U.S. Employment


Dixon, Peter B., Johnson, Martin, Rimmer, Maureen T., Contemporary Economic Policy


1. INTRODUCTION

In 2005, there were 7.3 million illegal immigrants working in the United States, 5% of total employment. Public attitudes on illegal immigrants working in the United States, 5% of total employment. Public attitudes on illegal immigrants vary from the view that they are impoverishing low-income legal residents by depriving them of jobs to the view that they are vital to the economy because they perform tasks that legal residents are unwilling to undertake. The illegal-immigrant issue is now a major component of the political debate with policy suggestions ranging from mass deportation to legalization and amnesty.

There is a vast literature on economic aspects of immigration to the United States, dealing with: the causes of immigration flows; (1) the performance of immigrants in the U.S. economy; (2) and the effects of immigrants on the economy. (3) Perhaps reflecting data limitations, the literature on illegal immigrants is small and deals mainly with the causes of illegal flows (4) and the effects on flows of policy interventions such as heightened border security. (5) Apart from estimates of the effects of illegal immigrants on public sector budgets, (6) there is little quantitative analysis of their economy-wide consequences.

The aim of this paper is to contribute to this comparatively underdeveloped aspect of the literature on illegal immigration. Applying USAGE, a multisectoral model, we provide quantitative analysis of the economy-wide effects of three broad approaches to reducing illegal immigrants in U.S. employment: tighter border security; taxes on employers; and vigorous prosecution of employers. In explaining USAGE results, we rely on familiar diagrams and back-of-the-envelope calculations rather than requiring readers to be familiar with USAGE details.

The paper is organized as follows. Section II gives brief background material on USAGE highlighting points that will be useful in interpreting results in later sections. Sections III and IV contain the main results, and Section V provides sensitivity analysis. Concluding remarks are in Section VI.

II. THE USAGE MODEL AND KEY MACRO ASSUMPTIONS

USAGE is a dynamic CGE model of the United States developed in collaboration with the U.S. International Trade Commission. The theoretical structure of USAGE is similar to that of Australia's MONASH model, Dixon and Rimmer (2002). However, most applications of models such as USAGE and MONASH require theoretical adaptations and database changes. For this study, we created a version of USAGE with 38 industries and 51 occupations. The first 50 occupations are used by the 38 industries. The 51st occupation is employment outside the United States of potential illegal immigrants. The introduction of this 51st occupation facilities the specification of flows of illegal immigrants.

A USAGE simulation of the effects of a policy shock requires two runs of the model: a basecase run and a policy run. The basecase is intended to be a plausible forecast while the policy run generates deviations away from the basecase caused by the policy under consideration. As well as policy changes, the policy run introduces macroeconomic and labor-market assumptions. The main assumptions underlying the results in Sections III-V are as follows:

A. Production Technologies and Household Preferences

In our policy runs, all technology and preference variables are exogenous and kept on their basecase paths. Thus, we assume that these variables are unaffected by changes in immigration policy.

B. Inflation

The price deflator for GDP is exogenous in policy runs and set on its basecase path. Thus, we assume that changes in immigration policy have no effect on inflation.

C. Investment and Rates of Return

In policy runs, USAGE allows for short-run divergences in industry rates of return from their basecase levels. Short-run increases/decreases in rates of return cause increases/decreases in investment and capital stocks. Thereby rate-of-return divergences in early years are gradually eroded.

D. Private and Public Consumption, and the Public-sector Deficit

In policy runs, the average propensities of legal residents and illegal immigrants to consume out of their disposable incomes are exogenous and set on their basecase paths. We assume that illegal immigrants remit their savings to their home countries.

For public consumption, we assume that expenditure per capita on both legal residents and illegal immigrants is proportional to private consumption per capita by legal residents. The factor of proportionality for illegal immigrants is set at 0.49 times the value applying to legal residents. (7) Thus, we assume that as legal residents become richer they demand more services from the public sector; that illegal immigrants cannot be prevented from enjoying improvements in public amenities made available for legal residents; but that not all government services available to legal residents are available to illegal immigrants.

On the income-side of the public sector budget, we assume that income-tax rates adjust to ensure that the public-sector deficit follows its basecase path.

E. The Labor Market

Technical details of the USAGE labor-market specification are in Dixon and Rimmer (2008). Here, we provide an overview, sufficient for understanding the results presented in later sections.

USAGE contains demand and supply equations for jobs classified by occupation (o, the 50 occupations in Table 1); birthplace (b, domestic/foreign); and legal status (s, legal/illegal). The demand equations were developed by assuming that industries minimize their costs per effective unit of labor input. An effective unit is defined as a 3-stage nested CES function of (o, b, s) inputs. At the top level, inputs with different occupational characteristics are poor substitutes (e.g., Cooks are poor substitutes for Carpenters); at the second level, inputs with the same occupational characteristic but different legal statuses are medium substitutes; and at the third level, inputs with the same occupational and legal characteristics but different birthplace characteristics are good substitutes. The three substitution elasticities are 0.35, 5.0, and 7.5. For this paper, the most important of these is the legal/illegal substitution elasticity (5.0), discussed further in Sections III.C and V.B.

TABLE 1
Occupational Data for 2005 and SR Deviation Results for 29

                         Illegal           % Deviation in 2019
                         Immigrants
                         % of Labor Costs  Legal Jobs (2)  Legal Wage
                         in 2005                           (3)

1. Cook                              15.6            4.20        1.89

2. Grounds maintenance               24.8            7.45        3.19

3. House keeping and                 22.0            6.56        2.82
cleaning

4. Janitor and building              10.4            2.31        1.19
cleaner

5. Misc. agriculture                 34.3           10.70         455
worker

6. Construction laborer              23.9            7.10        3.16

7. Transport packer                  24.6            7.37        3.19

8. Carpenter                         15.1            3.90        1.92

9. Transport laborer                  7.2            1.09        0.71

10. Cashier                           4.7            0.31        0.43

11. Food serving                      6.4            0.88        0.62

12. Transport driver                  4.0           -0.09        0.25

13. Waiter                            5.7            0.64        0.53

14. Production, misc.                 8.3            1.07        0.72
assistant

15. Food prep. worker                13.3            3.42        1.61

16. Painter                          24.9            7.46        3.31

17. Dishwasher                       22.7            6.83        2.86

18. Construction,                    24.8            7.42        3.30
helper

19. Retail sales                      2.4           -0.50        0.11

20. Production, helper               20.4            5.54        2.52

21. Packing machine                  23.6            6.88        3.01
oper.

22. Butcher                          21.0            6.20        2.74

23. Stock clerk                       4.6            0.26        0.40

24. Child care                        5.2            0.56        0.51

25. Misc. food prep.                 14.5            3.80        1.74

26. Dry wall installer               35.8           11.43        4.87

27. Nursing                           2.8           -0.01        0.29

28. Industrial truck                  8.5            1.47        0.87
oper.

29. Transport, cleaner               15.8            4.24        1.93

30. Automotive repairs                6.3            0.88        0.64

31. Sew. Machine oper.               18.8            4.95        2.39

32. Concrete mason                   22.6            6.61        3.00

33. Roofer                           28.2            8.64        3.78

34. Plumber                           7.1            1.07        0.80

35. Personal care                     5.7            0.91 

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